IIoT-Enabled Apparel Demand Forecasting: A Random Forest Approach Mining E-Commerce Reviews

Abstract

Within the industrial internet of things (IIoT) ecosystems, apparel manufacturers face the dual challenge of integrating high-velocity consumer feedback streams from e-commerce platforms and translating them into real-time, high-fidelity demand forecasts. This study presents an IIoT-native framework that employs random forest regression (RFR) to fuse multi-modal review features—sentiment polarity, key phrases, and aggregate ratings—collected via edge gateways from 1100 men's garments on JD.com. Innovatively, the proposed framework not only outperforms traditional linear models such as ordinary least squares (OLS) and multiple linear regression (MLR) in terms of predictive accuracy but also demonstrates robustness to noise and outliers across heterogeneous product categories. The cloud-hosted RFR model achieves an R² of 0.9442 and root mean square error (RMSE) of 105.76, representing a 5.6% and 35.9% improvement over MLR and OLS in RMSE, respectively. This study provides the first multi-category empirical evidence that fusing review-level sentiment, key phrases, and ratings via RFR yields significant enhancements in IIoT-scale apparel demand forecasting.